Improving accuracy of long-term prognostics of PEMFC stack to estimate remaining useful life

Abstract

Proton Exchange Membrane Fuel cells (PEMFC) are energy systems that facilitate electrochemical reactions to create electrical energy from chemical energy of hydrogen. PEMFC are promising source of renewable energy that can operate on low temperature and have the advantages of high power density and low pollutant emissions. However, PEMFC technology is still in the developing phase, and its large-scale industrial deployment requires increasing the life span of fuel cells and decreasing their exploitation costs. In this context, Prognostics and Health Management of fuel cells is an emerging field, which aims at identifying degradation at early stages and estimating the Remaining Useful Life (RUL) for life cycle management. Indeed, due to prognostics capability, the accurate estimates of RUL enables safe operation of the equipment and timely decisions to prolong its life span. This paper contributes data-driven prognostics of PEMFC by an ensemble of constraint based Summation Wavelet-Extreme Learning Machine (SW-ELM) algorithm to improve accuracy and robustness of long-term prognostics. The SW-ELM is used for ensemble modeling due to its enhanced applicability for real applications as compared to conventional data-driven algorithms. The proposed prognostics model is validated on run-to-failure data of PEMFC stack, which had the life span of 1750 hours. The results confirm capability of the prognostics model to achieve accurate RUL estimates.